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- Author or Editor: Wei Yu x
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Abstract
With a particular focus on the Siberian storm track, this study provides new insights into variations in the warm Arctic–cold Eurasia (WACE) temperature anomaly pattern by using reanalysis data. The results show that the Siberian storm track has a significant out-of-phase relationship with both the WACE pattern and Ural blocking on the interannual time scale. The strengthened WACE pattern can weaken the Siberian storm track through a suppression of the low-level atmospheric baroclinicity over midlatitude Eurasia. The weakened Siberian storm track can contribute to the WACE pattern through feedback forcing from synoptic-scale eddies, which can also create favorable conditions for the development of Ural blocking. Composite temporal evolution reveals that the strongest cold Arctic–warm Eurasia pattern is preceded by the peak of the Siberian storm track. The Ural cyclonic circulation reaches its maximum amplitude on the peak day of the Siberian storm track strength and continues to persist for one day with the maximum amplitude due to the feedback forcing resulting from the Siberian storm track. On the intraseasonal time scale, the occurrence of the Siberian storm track activity can serve as an early indication of the diminished Ural blocking and WACE pattern.
Significance Statement
Because of the high impacts of the warm Arctic–cold Eurasia (WACE) pattern on public safety, socioeconomic development, and the economy, it is crucial to enhance our understanding of variations in the WACE pattern. This paper specifically investigates the impact of internal atmospheric variability on the WACE pattern, focusing on a pronounced negative correlation between the Siberian storm track and the WACE pattern. Daily composites also reveal that Siberian storm track activities can promote a strong cold Arctic–warm Eurasia pattern by maintaining the strength of the quasi-stationary Ural cyclonic circulation. As such, paying close attention to Siberian storm track activities may hold the promise to improve the prediction of the strength of the WACE pattern.
Abstract
With a particular focus on the Siberian storm track, this study provides new insights into variations in the warm Arctic–cold Eurasia (WACE) temperature anomaly pattern by using reanalysis data. The results show that the Siberian storm track has a significant out-of-phase relationship with both the WACE pattern and Ural blocking on the interannual time scale. The strengthened WACE pattern can weaken the Siberian storm track through a suppression of the low-level atmospheric baroclinicity over midlatitude Eurasia. The weakened Siberian storm track can contribute to the WACE pattern through feedback forcing from synoptic-scale eddies, which can also create favorable conditions for the development of Ural blocking. Composite temporal evolution reveals that the strongest cold Arctic–warm Eurasia pattern is preceded by the peak of the Siberian storm track. The Ural cyclonic circulation reaches its maximum amplitude on the peak day of the Siberian storm track strength and continues to persist for one day with the maximum amplitude due to the feedback forcing resulting from the Siberian storm track. On the intraseasonal time scale, the occurrence of the Siberian storm track activity can serve as an early indication of the diminished Ural blocking and WACE pattern.
Significance Statement
Because of the high impacts of the warm Arctic–cold Eurasia (WACE) pattern on public safety, socioeconomic development, and the economy, it is crucial to enhance our understanding of variations in the WACE pattern. This paper specifically investigates the impact of internal atmospheric variability on the WACE pattern, focusing on a pronounced negative correlation between the Siberian storm track and the WACE pattern. Daily composites also reveal that Siberian storm track activities can promote a strong cold Arctic–warm Eurasia pattern by maintaining the strength of the quasi-stationary Ural cyclonic circulation. As such, paying close attention to Siberian storm track activities may hold the promise to improve the prediction of the strength of the WACE pattern.
Abstract
Area-averaged estimates of Cn 2 from high-resolution numerical weather prediction (NWP) model output are produced from local estimates of the spatial structure functions of refractive index with corrections for the inherent smoothing and filtering effects of the underlying NWP model. The key assumptions are the existence of a universal statistical description of small-scale turbulence and a locally universal spatial filter for the NWP model variables. Under these assumptions, spatial structure functions of the NWP model variables can be related to the structure functions of the atmospheric variables and extended to the smaller underresolved scales. The shape of the universal spatial filter is determined by comparisons of model structure functions with the climatological spatial structure function determined from an archive of aircraft data collected in the upper troposphere and lower stratosphere. This method of computing Cn 2 has an important advantage over more traditional methods that are based on vertical differences because the structure function–based estimates avoid reference to the turbulence outer length scale. To evaluate the technique, NWP model–derived structure-function estimates of Cn 2 are compared with nighttime profiles of Cn 2 derived from temperature structure-function sensors attached to a rawinsonde (thermosonde) near Holloman Air Force Base in the United States.
Abstract
Area-averaged estimates of Cn 2 from high-resolution numerical weather prediction (NWP) model output are produced from local estimates of the spatial structure functions of refractive index with corrections for the inherent smoothing and filtering effects of the underlying NWP model. The key assumptions are the existence of a universal statistical description of small-scale turbulence and a locally universal spatial filter for the NWP model variables. Under these assumptions, spatial structure functions of the NWP model variables can be related to the structure functions of the atmospheric variables and extended to the smaller underresolved scales. The shape of the universal spatial filter is determined by comparisons of model structure functions with the climatological spatial structure function determined from an archive of aircraft data collected in the upper troposphere and lower stratosphere. This method of computing Cn 2 has an important advantage over more traditional methods that are based on vertical differences because the structure function–based estimates avoid reference to the turbulence outer length scale. To evaluate the technique, NWP model–derived structure-function estimates of Cn 2 are compared with nighttime profiles of Cn 2 derived from temperature structure-function sensors attached to a rawinsonde (thermosonde) near Holloman Air Force Base in the United States.
Abstract
The southwest vortex (SWV) is a critical weather system in China, but our knowledge of this system remains incomplete. Here, we investigate the cloud properties in the SWV. First, we search for the SWVs with time steps and center locations that are consistent between the SWV yearbook and ERA-Interim reanalysis data. Second, we supplement these SWVs’ life spans and movement paths. Third, we relocate the Fengyun (FY) satellite FY-4A cloud retrievals in the 10° × 10° region centered on each SWV and analyze the cloud occurrence frequency (COF), cloud-top height (CTH), and cloud optical thickness (COT). A distribution mode of cloud types is summarized from the COFs, with water clouds, supercooled clouds, mixed clouds, ice clouds, cirrus clouds, and overlap clouds occurring sequentially from west to east. The CTH probability density (PD) distribution features a significant north–south difference. In addition, the COT PD distributions exhibit a common trend: with increasing COT, the PD increases rapidly and then slowly before peaking, whereupon the PD decreases abruptly. From spring to summer, the region with the highest convective COF shifts from the northeast to the northwest, and an east–west gradient of the convective COF appears in autumn and winter. Furthermore, we investigate the cloud properties during SWV-related heavy rainfall. Heavy rain occurs mainly in the west of the SWV, and convective clouds are mainly in the northwest, partly in the southwest and near the SWV center. The average CTH in heavy rainfall is generally higher than 6 km, and the average COT is greater than 20.
Significance Statement
The southwest vortex (SWV) is an important weather system in China. However, we do not yet comprehensively know this weather system. The cloud properties can indicate the structures of weather systems and are key parameters in numerical weather prediction (NWP) models. Thus, investigating cloud properties is necessary and meaningful to understand the SWV and accurately predict SWV-related precipitation in NWP models. In this paper, a typical distribution mode of six cloud types in the SWV is summarized from the cloud occurrence frequency, and the distribution features of convective clouds, cloud-top height, and cloud optical thickness in the SWV are analyzed. Furthermore, the cloud properties in SWV-related heavy rain are also studied.
Abstract
The southwest vortex (SWV) is a critical weather system in China, but our knowledge of this system remains incomplete. Here, we investigate the cloud properties in the SWV. First, we search for the SWVs with time steps and center locations that are consistent between the SWV yearbook and ERA-Interim reanalysis data. Second, we supplement these SWVs’ life spans and movement paths. Third, we relocate the Fengyun (FY) satellite FY-4A cloud retrievals in the 10° × 10° region centered on each SWV and analyze the cloud occurrence frequency (COF), cloud-top height (CTH), and cloud optical thickness (COT). A distribution mode of cloud types is summarized from the COFs, with water clouds, supercooled clouds, mixed clouds, ice clouds, cirrus clouds, and overlap clouds occurring sequentially from west to east. The CTH probability density (PD) distribution features a significant north–south difference. In addition, the COT PD distributions exhibit a common trend: with increasing COT, the PD increases rapidly and then slowly before peaking, whereupon the PD decreases abruptly. From spring to summer, the region with the highest convective COF shifts from the northeast to the northwest, and an east–west gradient of the convective COF appears in autumn and winter. Furthermore, we investigate the cloud properties during SWV-related heavy rainfall. Heavy rain occurs mainly in the west of the SWV, and convective clouds are mainly in the northwest, partly in the southwest and near the SWV center. The average CTH in heavy rainfall is generally higher than 6 km, and the average COT is greater than 20.
Significance Statement
The southwest vortex (SWV) is an important weather system in China. However, we do not yet comprehensively know this weather system. The cloud properties can indicate the structures of weather systems and are key parameters in numerical weather prediction (NWP) models. Thus, investigating cloud properties is necessary and meaningful to understand the SWV and accurately predict SWV-related precipitation in NWP models. In this paper, a typical distribution mode of six cloud types in the SWV is summarized from the cloud occurrence frequency, and the distribution features of convective clouds, cloud-top height, and cloud optical thickness in the SWV are analyzed. Furthermore, the cloud properties in SWV-related heavy rain are also studied.
Abstract
Based on daily meteorological observation data in South China (SC) from 1967 to 2018, the spatiotemporal characteristics of the precipitation in SC over the past 52 years were studied. Only 8% of the stations showed a significant increase in annual rainfall, and there was no significant negative trend at any weather stations at a confidence level of 90%. Monthly rainfall showed the most significant decreasing and increasing trends in April and November, respectively. During the entire flooding season from April to September, the monthly rainfall at the weather stations in the coastal areas showed almost no significant change. The annual rainfall gradually decreased toward the inland area with the central and coastal areas of Guangdong Province as the high-value rainfall center. By using the empirical orthogonal function decomposition method, it was found that the two main monthly rainfall modes had strong annual signals. The first modal spatial distribution was basically consistent with the average annual rainfall distribution. Based on the environmental background analysis, it was found that during the flooding season the main water vapor to SC was transported by the East Asian summer monsoon and the Indian summer monsoon. In late autumn and winter, the prevailing wind from northeastern China could not bring much water vapor to SC and led to little precipitation in these two seasons. The spatial distribution of precipitation in SC during summer was more consistent with the moisture flux divergence distribution of the bottom layer from 925 to 1000 hPa rather than that of the layer from 700 to 1000 hPa.
Abstract
Based on daily meteorological observation data in South China (SC) from 1967 to 2018, the spatiotemporal characteristics of the precipitation in SC over the past 52 years were studied. Only 8% of the stations showed a significant increase in annual rainfall, and there was no significant negative trend at any weather stations at a confidence level of 90%. Monthly rainfall showed the most significant decreasing and increasing trends in April and November, respectively. During the entire flooding season from April to September, the monthly rainfall at the weather stations in the coastal areas showed almost no significant change. The annual rainfall gradually decreased toward the inland area with the central and coastal areas of Guangdong Province as the high-value rainfall center. By using the empirical orthogonal function decomposition method, it was found that the two main monthly rainfall modes had strong annual signals. The first modal spatial distribution was basically consistent with the average annual rainfall distribution. Based on the environmental background analysis, it was found that during the flooding season the main water vapor to SC was transported by the East Asian summer monsoon and the Indian summer monsoon. In late autumn and winter, the prevailing wind from northeastern China could not bring much water vapor to SC and led to little precipitation in these two seasons. The spatial distribution of precipitation in SC during summer was more consistent with the moisture flux divergence distribution of the bottom layer from 925 to 1000 hPa rather than that of the layer from 700 to 1000 hPa.
Abstract
This study explores the mechanisms responsible for valley precipitation enhancement over Da-Tun Mountain under the prevailing northeasterly monsoonal flow. Da-Tun Mountain, located adjacent to the northern coast of Taiwan, is a small-scale (15 km), concave-like terrain feature with two ridge arms and a funnel-shaped valley. A typical valley precipitation enhancement event that occurred on 13 December 2018 was chosen for detailed analyses. Upstream conditions were characterized by the absence of convective available potential energy with a large-Froude-number (>1) flow regime. Observational and modeling results indicate a consistent, important signature of flow splitting due to partial blocking as the low-level northeasterly flow encountered the ridge arms. Fine-scale structures of airflow and precipitation evident from the simulations further reveal that the deflected flows over the two ridge arms interacted with each other to produce lateral convergence and enhanced precipitation inside the valley. The smaller-scale splitting flows tended to occur over the ridge arms as upstream moist Froude number decreased from relatively higher (5–11) to lower (3–5) values due to the temporal change in moist static stability. Quantitative diagnoses of vertical velocities performed over the region of primary precipitation support that upward motions associated with lateral convergence greatly overwhelmed the upslope-forced lifting over the valley region during the valley precipitation enhancement periods. However, vertical motions over the ridge arms with steeper slopes were dominantly contributed by the upslope forcing, but their intensities were also modulated by the flow-splitting-induced divergence.
Significance Statement
Many mountain ranges around the world exhibit a concave-like terrain feature with various spatial scales and orientations. Orographic modulations of rainfall by concave mountains are of great importance to local weather, as torrential rainfall has been frequently reported over these ridges in different geographical locations. This study aims to advance our knowledge of precipitation mechanisms over Da-Tun Mountain, a small-scale concave topography located in northern Taiwan. Observational and modeling analyses reveal evidence of splitting flows over different ridge arms of this mountain barrier. These smaller-scale splitting flows and their interactions play important roles in modulating the intensity of upslope-forced lifting and contributing to valley precipitation enhancement. These identified processes are anticipated to be commonly active over highly three-dimensional, concave-like topography.
Abstract
This study explores the mechanisms responsible for valley precipitation enhancement over Da-Tun Mountain under the prevailing northeasterly monsoonal flow. Da-Tun Mountain, located adjacent to the northern coast of Taiwan, is a small-scale (15 km), concave-like terrain feature with two ridge arms and a funnel-shaped valley. A typical valley precipitation enhancement event that occurred on 13 December 2018 was chosen for detailed analyses. Upstream conditions were characterized by the absence of convective available potential energy with a large-Froude-number (>1) flow regime. Observational and modeling results indicate a consistent, important signature of flow splitting due to partial blocking as the low-level northeasterly flow encountered the ridge arms. Fine-scale structures of airflow and precipitation evident from the simulations further reveal that the deflected flows over the two ridge arms interacted with each other to produce lateral convergence and enhanced precipitation inside the valley. The smaller-scale splitting flows tended to occur over the ridge arms as upstream moist Froude number decreased from relatively higher (5–11) to lower (3–5) values due to the temporal change in moist static stability. Quantitative diagnoses of vertical velocities performed over the region of primary precipitation support that upward motions associated with lateral convergence greatly overwhelmed the upslope-forced lifting over the valley region during the valley precipitation enhancement periods. However, vertical motions over the ridge arms with steeper slopes were dominantly contributed by the upslope forcing, but their intensities were also modulated by the flow-splitting-induced divergence.
Significance Statement
Many mountain ranges around the world exhibit a concave-like terrain feature with various spatial scales and orientations. Orographic modulations of rainfall by concave mountains are of great importance to local weather, as torrential rainfall has been frequently reported over these ridges in different geographical locations. This study aims to advance our knowledge of precipitation mechanisms over Da-Tun Mountain, a small-scale concave topography located in northern Taiwan. Observational and modeling analyses reveal evidence of splitting flows over different ridge arms of this mountain barrier. These smaller-scale splitting flows and their interactions play important roles in modulating the intensity of upslope-forced lifting and contributing to valley precipitation enhancement. These identified processes are anticipated to be commonly active over highly three-dimensional, concave-like topography.
Abstract
Tropical cyclone (TC) genesis is a problem of great significance in climate and weather research. Although various environmental conditions necessary for TC genesis have been recognized for a long time, prediction of TC genesis remains a challenge due to complex and stochastic processes involved during TC genesis. Different from traditional statistical and dynamical modeling of TC genesis, in this study, a machine learning framework is developed to determine whether a mesoscale convective system (MCS) would evolve into a tropical cyclone. The machine learning models 1) are built upon a number of essential environmental predictors associated with MCSs/TCs, 2) predict whether MCSs can become TCs at different lead times, and 3) provide information about the relative importance of each predictor, which can be conducive to discovering new aspects of TC genesis. The results indicate that the machine learning classifier, AdaBoost, is able to achieve a 97.2% F1-score accuracy in predicting TC genesis over the entire tropics at a 6-h lead time using a comprehensive set of environmental predictors. A robust performance can still be attained when the lead time is extended to 12, 24, and 48 h, and when this machine learning classifier is separately applied to the North Atlantic Ocean and the western North Pacific Ocean. In contrast, the conventional approach based on the genesis potential index can have no more than an 80% F1-score accuracy. Furthermore, the machine learning classifier suggests that the low-level vorticity and genesis potential index are the most important predictors to TC genesis, which is consistent with previous discoveries.
Abstract
Tropical cyclone (TC) genesis is a problem of great significance in climate and weather research. Although various environmental conditions necessary for TC genesis have been recognized for a long time, prediction of TC genesis remains a challenge due to complex and stochastic processes involved during TC genesis. Different from traditional statistical and dynamical modeling of TC genesis, in this study, a machine learning framework is developed to determine whether a mesoscale convective system (MCS) would evolve into a tropical cyclone. The machine learning models 1) are built upon a number of essential environmental predictors associated with MCSs/TCs, 2) predict whether MCSs can become TCs at different lead times, and 3) provide information about the relative importance of each predictor, which can be conducive to discovering new aspects of TC genesis. The results indicate that the machine learning classifier, AdaBoost, is able to achieve a 97.2% F1-score accuracy in predicting TC genesis over the entire tropics at a 6-h lead time using a comprehensive set of environmental predictors. A robust performance can still be attained when the lead time is extended to 12, 24, and 48 h, and when this machine learning classifier is separately applied to the North Atlantic Ocean and the western North Pacific Ocean. In contrast, the conventional approach based on the genesis potential index can have no more than an 80% F1-score accuracy. Furthermore, the machine learning classifier suggests that the low-level vorticity and genesis potential index are the most important predictors to TC genesis, which is consistent with previous discoveries.
Abstract
Soil moisture heterogeneity can induce mesoscale circulations due to differential heating between dry and wet surfaces, which can, in turn, trigger precipitation. In this work, we conduct cloud-permitting simulations over a 100 km × 25 km idealized land surface, with the domain split equally between a wet region and a dry region, each with homogeneous soil moisture. In contrast to previous studies that prescribed initial atmospheric profiles, each simulation is run with fixed soil moisture for 100 days to allow the atmosphere to equilibrate to the given land surface rather than prescribing the initial atmospheric profile. It is then run for one additional day, allowing the soil moisture to freely vary. Soil moisture controls the resulting precipitation over the dry region through three different mechanisms: as the dry domain gets drier, (i) the mesoscale circulation strengthens, increasing water vapor convergence over the dry domain, (ii) surface evaporation declines over the dry domain, decreasing water vapor convergence over the dry domain, and (iii) precipitation efficiency declines due to increased reevaporation, meaning proportionally less water vapor over the dry domain becomes surface precipitation. We find that the third mechanism dominates when soil moisture is small in the dry domain: drier soils ultimately lead to less precipitation in the dry domain due to its impact on precipitation efficiency. This work highlights an important new mechanism by which soil moisture controls precipitation, through its impact on precipitation reevaporation and efficiency.
Abstract
Soil moisture heterogeneity can induce mesoscale circulations due to differential heating between dry and wet surfaces, which can, in turn, trigger precipitation. In this work, we conduct cloud-permitting simulations over a 100 km × 25 km idealized land surface, with the domain split equally between a wet region and a dry region, each with homogeneous soil moisture. In contrast to previous studies that prescribed initial atmospheric profiles, each simulation is run with fixed soil moisture for 100 days to allow the atmosphere to equilibrate to the given land surface rather than prescribing the initial atmospheric profile. It is then run for one additional day, allowing the soil moisture to freely vary. Soil moisture controls the resulting precipitation over the dry region through three different mechanisms: as the dry domain gets drier, (i) the mesoscale circulation strengthens, increasing water vapor convergence over the dry domain, (ii) surface evaporation declines over the dry domain, decreasing water vapor convergence over the dry domain, and (iii) precipitation efficiency declines due to increased reevaporation, meaning proportionally less water vapor over the dry domain becomes surface precipitation. We find that the third mechanism dominates when soil moisture is small in the dry domain: drier soils ultimately lead to less precipitation in the dry domain due to its impact on precipitation efficiency. This work highlights an important new mechanism by which soil moisture controls precipitation, through its impact on precipitation reevaporation and efficiency.
Abstract
China, the second largest economy in the world, covers a large area spanning multiple climate zones, with varying economic conditions across regions. Given this variety in climate and economic conditions, global warming is expected to have heterogeneous economic impacts across the country. This study uses annual average temperature to conduct an empirical research from a top-down perspective to evaluate the nonlinear impacts of temperature change on aggregate economic output in China. We find that there is an inverted U-shaped relationship between temperature and economic growth at the provincial level, with a turning point at 12.2°C. The regional and national economic impacts are projected under the shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs). As future temperature rises, the economic impacts are positive in the northeast, north, and northwest regions but negative in the south, east, central, and southwest regions. Based on SSP5, the decrement in the GDP per capita of China would reach 16.0% under RCP2.6 and 27.0% under RCP8.5.
Abstract
China, the second largest economy in the world, covers a large area spanning multiple climate zones, with varying economic conditions across regions. Given this variety in climate and economic conditions, global warming is expected to have heterogeneous economic impacts across the country. This study uses annual average temperature to conduct an empirical research from a top-down perspective to evaluate the nonlinear impacts of temperature change on aggregate economic output in China. We find that there is an inverted U-shaped relationship between temperature and economic growth at the provincial level, with a turning point at 12.2°C. The regional and national economic impacts are projected under the shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs). As future temperature rises, the economic impacts are positive in the northeast, north, and northwest regions but negative in the south, east, central, and southwest regions. Based on SSP5, the decrement in the GDP per capita of China would reach 16.0% under RCP2.6 and 27.0% under RCP8.5.
Abstract
Generalized equilibrium feedback assessment (GEFA) is a potentially valuable multivariate statistical tool for extracting vegetation feedbacks to the atmosphere in either observations or coupled Earth system models. The reliability of GEFA at capturing the terrestrial impacts on regional climate is demonstrated here using the National Center for Atmospheric Research Community Earth System Model (CESM), with focus on North Africa. The feedback is assessed statistically by applying GEFA to output from a fully coupled control run. To reduce the sampling error caused by short data records, the traditional or full GEFA is refined through stepwise GEFA by dropping unimportant forcings. Two ensembles of dynamical experiments are developed for the Sahel or West African monsoon region against which GEFA-based vegetation feedbacks are evaluated. In these dynamical experiments, regional leaf area index (LAI) is modified either alone or in conjunction with soil moisture, with the latter runs motivated by strong regional soil moisture–LAI coupling. Stepwise GEFA boasts higher consistency between statistically and dynamically assessed atmospheric responses to land surface anomalies than full GEFA, especially with short data records. GEFA-based atmospheric responses are more consistent with the coupled soil moisture–LAI experiments, indicating that GEFA is assessing the combined impacts of coupled vegetation and soil moisture. Both the statistical and dynamical assessments reveal a negative vegetation–rainfall feedback in the Sahel associated with an atmospheric stability mechanism in CESM versus a weaker positive feedback in the West African monsoon region associated with a moisture recycling mechanism in CESM.
Abstract
Generalized equilibrium feedback assessment (GEFA) is a potentially valuable multivariate statistical tool for extracting vegetation feedbacks to the atmosphere in either observations or coupled Earth system models. The reliability of GEFA at capturing the terrestrial impacts on regional climate is demonstrated here using the National Center for Atmospheric Research Community Earth System Model (CESM), with focus on North Africa. The feedback is assessed statistically by applying GEFA to output from a fully coupled control run. To reduce the sampling error caused by short data records, the traditional or full GEFA is refined through stepwise GEFA by dropping unimportant forcings. Two ensembles of dynamical experiments are developed for the Sahel or West African monsoon region against which GEFA-based vegetation feedbacks are evaluated. In these dynamical experiments, regional leaf area index (LAI) is modified either alone or in conjunction with soil moisture, with the latter runs motivated by strong regional soil moisture–LAI coupling. Stepwise GEFA boasts higher consistency between statistically and dynamically assessed atmospheric responses to land surface anomalies than full GEFA, especially with short data records. GEFA-based atmospheric responses are more consistent with the coupled soil moisture–LAI experiments, indicating that GEFA is assessing the combined impacts of coupled vegetation and soil moisture. Both the statistical and dynamical assessments reveal a negative vegetation–rainfall feedback in the Sahel associated with an atmospheric stability mechanism in CESM versus a weaker positive feedback in the West African monsoon region associated with a moisture recycling mechanism in CESM.
Abstract
This study advances the practicality and stability of the traditional multivariate statistical method, generalized equilibrium feedback assessment (GEFA), for decomposing the key oceanic drivers of regional atmospheric variability, especially when available data records are short. An advanced stepwise GEFA methodology is introduced, in which unimportant forcings within the forcing matrix are eliminated through stepwise selection. Method validation of stepwise GEFA is performed using the CESM, with a focused application to northern and tropical Africa (NTA). First, a statistical assessment of the atmospheric response to each primary oceanic forcing is carried out by applying stepwise GEFA to a fully coupled control run. Then, a dynamical assessment of the atmospheric response to individual oceanic forcings is performed through ensemble experiments by imposing sea surface temperature anomalies over focal ocean basins. Finally, to quantify the reliability of stepwise GEFA, the statistical assessment is evaluated against the dynamical assessment in terms of four metrics: the percentage of grid cells with consistent response sign, the spatial correlation of atmospheric response patterns, the area-averaged seasonal cycle of response magnitude, and consistency in associated mechanisms between assessments. In CESM, tropical modes, namely El Niño–Southern Oscillation and the tropical Indian Ocean Basin, tropical Indian Ocean dipole, and tropical Atlantic Niño modes, are the dominant oceanic controls of NTA climate. In complementary studies, stepwise GEFA is validated in terms of isolating terrestrial forcings on the atmosphere, and observed oceanic and terrestrial drivers of NTA climate are extracted to establish an observational benchmark for subsequent coupled model evaluation and development of process-based weights for regional climate projections.
Abstract
This study advances the practicality and stability of the traditional multivariate statistical method, generalized equilibrium feedback assessment (GEFA), for decomposing the key oceanic drivers of regional atmospheric variability, especially when available data records are short. An advanced stepwise GEFA methodology is introduced, in which unimportant forcings within the forcing matrix are eliminated through stepwise selection. Method validation of stepwise GEFA is performed using the CESM, with a focused application to northern and tropical Africa (NTA). First, a statistical assessment of the atmospheric response to each primary oceanic forcing is carried out by applying stepwise GEFA to a fully coupled control run. Then, a dynamical assessment of the atmospheric response to individual oceanic forcings is performed through ensemble experiments by imposing sea surface temperature anomalies over focal ocean basins. Finally, to quantify the reliability of stepwise GEFA, the statistical assessment is evaluated against the dynamical assessment in terms of four metrics: the percentage of grid cells with consistent response sign, the spatial correlation of atmospheric response patterns, the area-averaged seasonal cycle of response magnitude, and consistency in associated mechanisms between assessments. In CESM, tropical modes, namely El Niño–Southern Oscillation and the tropical Indian Ocean Basin, tropical Indian Ocean dipole, and tropical Atlantic Niño modes, are the dominant oceanic controls of NTA climate. In complementary studies, stepwise GEFA is validated in terms of isolating terrestrial forcings on the atmosphere, and observed oceanic and terrestrial drivers of NTA climate are extracted to establish an observational benchmark for subsequent coupled model evaluation and development of process-based weights for regional climate projections.
